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Development in any country is impossible if reliable and affordable energy, safe water and sanitation, as well as telecommunication facilities, are not easily accessible. Artificial intelligence and machine learning techniques are now widely used in all branches of engineering to build and optimize systems. The combination of AI and engineering can indeed act as a real catalyst to achieve the UN SDGs. The volume editors present an analysis of different concepts and case studies in engineering disciplines such as chemical, civil, electrical, telecommunications and mechanical engineering, demonstrating how engineering systems and processes can leverage the power of AI to drive and achieve the UN SDGs. Topics covered include sustainable crop production and consumption, AI based clean water and sanitation monitoring, intelligent transport systems and achieving affordable and clean energy through AI and 5G powered internet of energy. Such a study is of paramount importance and is a valuable source of information for researchers, engineers, and policy makers to be able to better design and adopt AI enabled techniques in different engineering areas, with a view to catalyze the achievement of the UN SDGs.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- Authors -- Chapter 1: Artificial Intelligence within Industrial and Systems Engineering Framework -- 1.1 Introduction -- 1.2 Quantum Potential for AI -- 1.3 Old and New AI Achievements -- 1.4 Industrial Engineering Linkage -- 1.5 Historical Background of AI -- 1.6 Origin of Artificial Intelligence -- 1.7 Human Intelligence versus Machine Intelligence -- 1.8 Natural Language Dichotomies -- 1.9 The First Conference on Artificial Intelligence -- 1.10 Evolution of Smart Programs -- 1.11 Branches of Artificial Intelligence -- 1.12 Neural Networks -- 1.13 Emergence of Expert Systems -- References -- Chapter 2: Mathematics of Cantor Set for AI Searches -- 2.1 Introduction -- 2.2 Intelligent Searches -- 2.3 Backdrop for AI Searches -- 2.4 Mathematics of Cantor Set -- 2.4.1 Set Sectioning Technique of Cantor Set -- 2.4.2 Search of Asymmetrically Distributed Data -- 2.4.3 Derivation of the Mode Estimating Formula in Terms of Inclusive Graphic Skewness -- 2.4.4 Graphical Verification of the mod%-IGS Relationship -- 2.5 Results of Preliminary Research -- 2.6 Cantor and Binary Search Comparison of 1500 Data Point Files -- 2.7 Cantor and Binary Search Comparison of 150 Data Point Files -- 2.8 Comparison of the Binary Search and the Cantor Search for the various Database Sizes -- 2.9 Intelligent 1/n Sectioning of the Search Space -- References -- Chapter 3: Set-theoretic Systems for AI Applications -- 3.1 Set Systems in Problem Domains -- 3.2 Sets and Systems in Innovation -- 3.3 Ordered Pairs on Sets -- 3.4 Set Relations in Innovation Systems -- 3.5 Functions on Sets -- 3.6 Cardinality of Sets -- 3.7 Relationships of Set-to-System and Subset-to-Subsystem -- 3.8 Integration Mapping of Subsets -- 3.9 Model Reduction Approach.

481069_1_En_OFC -- Preface -- Contents -- About the Editors -- Assessment of the Heart Disease Using Soft Computing Methodology -- 1 Introduction -- 2 Methodology -- 3 System Analysis and Proposed Method -- 4 Result and Discussion -- 5 Conclusion -- References -- The Reasons for Rail Accident in India Using the Concept of Statistical Methods: An Analytical Approach -- 1 Introduction -- 2 Objective of the Work -- 3 Implementation -- 3.1 Analysis on Day, Month, and Year of Accident -- 4 Conclusion -- References -- Automatic Music Genre Detection Using Artificial Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 3.1 Dataset Formation -- 3.2 Feature Extraction -- 3.3 Multilayer Perceptron (MLP) -- 4 Experimental Results -- 4.1 Accuracy -- 4.2 Precision -- 4.3 Recall -- 4.4 Confusion Matrix -- 5 Conclusion and Future Work -- References -- Role of Ad Hoc and Sensor Network for Effective Business Communication -- 1 Introduction -- 2 Analogues Upbringing -- 3 Efficient Communication Will Assist an Organization -- 4 Values of Communication in the Organizational Structure -- 5 Defended Transmissions in MANET -- 6 Secure Transmissions in WSN -- 7 Conclusion -- References -- Implementation of Integrated Security System by Using Biometric Function in ATM Machine -- 1 Introduction -- 2 Proposed Work -- 2.1 Authentication Process in ATM -- 2.2 Working Process of the Proposed System -- 3 Performance Evaluation and Result Analysis -- 3.1 Face Recognition Result -- 4 Conclusion -- References -- DTSS and Clustering for Energy Conservation in Wireless Sensor Network -- 1 Introduction -- 2 Protocol of Timing Schedule -- 3 TDMA Scheduling -- 4 Wake-up/Sleep Mode Scheduling -- 5 Conclusions -- 6 Future Work -- References -- Load Distribution Challenges with Virtual Computing -- 1 Introduction -- 2 Proposed Methodology.

Computational Intelligence for Engineering Systems provides an overview and original analysis of new developments and advances in several areas of computational intelligence. Computational Intelligence have become the road-map for engineers to develop and analyze novel techniques to solve problems in basic sciences (such as physics, chemistry and biology) and engineering, environmental, life and social sciences. The contributions are written by international experts, who provide up-to-date aspects of the topics discussed and present recent, original insights into their own experience in these fields. The authors also include methods that apply to diverse fields such as manufacturing, tourism, power systems, computer science, robotics, chemistry, and biology. Topics include: Simulation and evolution of real and artificial life forms; Self-organization; Models of communication and social behaviors; Emergent collective behaviors and swarm intelligence; Adaptive, complex and biologically inspired systems; Power Systems ; Web-based Applications; Knowledge discovery; Intelligent Tutoring Systems ; Decision support Systems; Intelligent Tutoring Systems.

The Moroccan socio-economic context is more and more weakened by a dynamic of accelerated measures aimed at redressing the financial deficits of the state. The first emotionally impacted by this dynamic are parents and therefore their children who begin to live in a universe dominated by fluctuations and unforeseen events.This article will therefore explore the emotional situation of Moroccan pupils at the beginning of schooling and identify their needs in this area, in order to initiate a dynamic of engineering emotional intelligence in primary school.The study was carried out in 3 school groups in Casablanca on a mixed population of 160 pupils and was spread out over a month. The used tools comprised interviews with survey filling to measure the degree of awareness of emotions on the part of these pupils and their teachers. Subsequently, the population was subjected to two tests respectively of impulses control and team spirit.The results show an initial dominance of emotions in the studied population and demonstrate the major interest of a gradual integration of emotional intelligence courses in the primary cycle of education.

Intro -- About This Book -- Contents -- About the Authors -- 1 An Overview of Blasting Operations and Possible Techniques to Solve Environmental Issues of Blasting -- 1.1 Introduction -- 1.2 Blast Design -- 1.3 Environmental Effect Due to the Blasting -- 1.3.1 AOp or Air Blast -- 1.3.2 Ground Vibration -- 1.3.3 Flyrock -- 1.4 Blasting Effect Prediction -- 1.4.1 Prediction of AOp -- 1.4.2 Prediction of Ground Vibration -- 1.4.3 Prediction of Flyrock Distance -- 1.5 Prediction Methods by Computational Techniques -- 1.6 Blasting Solutions Enabled by the Blastiq™ Platform -- 1.6.1 Blast Design -- 1.6.2 Blast Control -- 1.6.3 BlastIQ™ Advanced Technologies -- 1.7 Conclusion Remarks -- References -- 2 Review of Empirical and Intelligent Techniques for Evaluating Rock Fragmentation Induced by Blasting -- 2.1 Introduction -- 2.2 Rock Fragmentation -- 2.3 Blastability -- 2.4 Fragmentation Measurement -- 2.5 Background of ML Models -- 2.5.1 Artificial Neural Network -- 2.5.2 Adaptive Neuro-Fuzzy Inference System (ANFIS) -- 2.5.3 Support Vector Machine (SVM) -- 2.5.4 Genetic Algorithm (GA) -- 2.6 Review of ML Models for Prediction of Rock Fragmentation -- 2.7 Discussion -- 2.8 Conclusion and Future Perspective -- References -- 3 Applications of AI and ML Techniques to Predict Backbreak and Flyrock Distance Resulting from Blasting -- 3.1 Introduction -- 3.2 Measurement of Flyrock -- 3.2.1 Flyrock -- 3.2.2 Backbreak -- 3.3 Concepts of Some AI Models -- 3.3.1 Artificial Neural Network (ANN) -- 3.3.2 ANFIS -- 3.3.3 Support Vector Machine (SVM) -- 3.3.4 ELM -- 3.3.5 PSO-ELM -- 3.4 Backbreak Prediction Using AI Techniques -- 3.5 Flyrock Prediction Using AI Techniques -- 3.6 Discussion -- 3.7 Conclusion -- References -- 4 Blast-Induced Air and Ground Vibrations: A Review of Soft Computing Techniques -- 4.1 Introduction -- 4.2 Ground Vibration -- 4.3 AOp.

The Systems Engineering Development of the Intelligence Information System allows military and customer intelligence agencies to consolidate and eliminate the current deficiencies. The Intelligence Information Systems development is through a systematic approach that identifies the key components needed over the systems life time. This development identifies needs and requirements necessary to prevent the problem with the development of poor, inefficient and costly systems. These needs lead to the intelligence systems organization formation. The needs also lead to the intelligence informations requirements and mission creation. The next step in the system development is to create the mission requirements, the operational requirements and the system maintenance concept. The intelligence systems operational and maintenance requirements are further broken down into smaller requirements with functional flow diagrams. These smaller requirements lead to the detail design and development of the intelligence information system. Once the development of the system begins, an integrated system and test plan ensures the intelligence systems fulfill the mission and customers needs. To manage and control the development of the information system, a system engineering plan is created. The Systems Engineering design of the Intelligence System identifies all primary components necessary to develop and maintain the system throughout its lifetime. ; Master of Science

International audience ; Artificial Intelligence (AI) techniques have been successfully applied in many areas of software engineering. The complexity of software systems has limited the application of AI techniques in many real world applications. This talk provides an insight into applications of AI techniques in software engineering and how innovative application of AI can assist in achieving ever competitive and firm schedules for software development projects as well as Information Technology (IT) management. The pros and cons of using AI techniques are investigated and specifically the application of AI in IT management, software application development and software security is considered. Organisations that build software applications do so in an environment characterised by limited resources, increased pressure to reduce cost and development schedules. Organisations demand to build software applications adequately and quickly. One approach to achieve this is to use automated software development tools from the very initial stage of software design up to the software testing and installation. Considering software testing as an example, automated software systems can assist in most software testing phases. On the hand data security, availability, privacy and integrity are very important issues in the success of a business operation. Data security and privacy policies in business are governed by business requirements and government regulations. AI can also assist in software security, privacy and reliability. Implementing data security using data encryption solutions remain at the forefront for data security. Many solutions to data encryption at this level are expensive, disruptive and resource intensive. AI can be used for data classification in organizations. It can assist in identifying and encrypting only the relevant data thereby saving time and processing power. Without data classification organizations using encryption process would simply encrypt everything and consequently impact users more than ...

This paper presents an anthropological study of knowledge production in the expert systems community within AI. Expert systems are built by knowledge engineers, specialists in the task known as `knowledge acquisition'. This is a complex process of interpretation and translation; not surprisingly (to an anthropologist, at least), it presents a troublesome `bottleneck'. However, knowledge engineers have a different perspective on why this is so. Typically positivist in approach, they see knowledge acquisition as conceptually straightforward. In their view, it is difficult, not because of the nature of knowledge or the complexity of the process, but rather because it requires extended face-to-face interaction between knowledge engineer and expert. Believing that automation will `get around' the inexact and uncontrollable nature of this interaction, they seek to automate it. Drawing on ethnographic material, the paper explores the knowledge engineers' epistemological stance, noting its characteristic deletions, and suggesting that they are reflected in the resultant technology.